Shared resource

About: Shared resource is a research topic. Over the lifetime, 7536 publications have been published within this topic receiving 123491 citations. The topic is also known as: network share.

How distributed data mining tasks can thrive as knowledge services

Abstract: IntroductionComputer science applications are becoming more and more network centric, ubiquitous, knowledge intensive, and computing demanding. This trend will result soon in an ecosystem of pervasive applications and services that professionals and end-users can exploit everywhere. Recently, collections of IT services and applications, such as Web services and Cloud computing services, became available opening the way for accessing computing services as public utilities, like water, gas and electricity.Key technologies for implementing that perspective are Cloud computing and Web services, semantic Web and ontologies, pervasive computing, P2P systems, Grid computing, ambient intelligence architectures, data mining and knowledge discovery tools, Web 2.0 facilities, mashup tools, and decentralized programming models. In fact, it is mandatory to develop solutions that integrate some or many of those technologies to provide future knowledge-intensive software utilities. The Grid paradigm can represent a key component of the future Internet, a cyber infrastructure for efficiently supporting that scenario.Grid and Cloud computing are evolved models of distributed computing and parallel processing technologies. The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. In the area of Grid computing a proposed approach in accordance with the trend outlined above is the Service-Oriented Knowledge Utilities (SOKU) model that envisions the integrated use of a set of technologies that are considered as a solution to information, knowledge and communication needs of many knowledge-based industrial and business applications. The SOKU approach stems from the necessity of providing knowledge and processing capabilities to everybody, thus supporting the advent of a competitive knowledge-based economy. Although the SOKU model is not yet implemented, Grids are increasingly equipped with data management tools, semantic technologies, complex work-flows, data mining features and other Web intelligence approaches. Similar efforts are currently devoted to develop knowledge and intelligent Clouds. These technologies can facilitate the process of having Grids and Clouds as strategic components for supporting pervasive knowledge intensive applications and utilities.Grids were originally designed for dealing with problems involving large amounts of data and/or compute-intensive applications. Today, however, Grids enlarged their horizon as they are going to run business applications supporting consumers and end-users. To face those new challenges, Grid environments must support adaptive knowledge management and data analysis applications by offering resources, services, and decentralized data access mechanisms. In particular, according to the service-oriented architecture (SOA) model, data mining tasks and knowledge discovery processes can be delivered as services in Grid-based infrastructures.Through a service-based approach we can define integrated services for supporting distributed business intelligence tasks in Grids. Those services can address all the aspects that must be considered in data mining and in knowledge discovery processes such as data selection and transport, data analysis, knowledge models representation and visualization. We worked in this direction for providing Grid-based architectures and services for distributed knowledge discovery such as the Knowledge Grid the Weka4WS toolkit, and mobile Grid services for data mining.Here we describe a strategy and a model based on the use of services for the design of distributed knowledge discovery services and discuss how Grid frameworks, such those mentioned above, can be developed as a collection of services and how they can be used to develop distributed data analysis tasks and knowledge discovery processes using the SOA model.

User-Centric Edge Sharing Mechanism in Software-Defined Ultra-Dense Networks

Abstract: The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

Timed model checking with abstractions: towards worst-case response time analysis in resource-sharing manycore systems

Abstract: Multicore architectures are increasingly used nowadays in embedded real-time systems. Parallel execution of tasks feigns the possibility of a massive increase in performance. However, this is usually not achieved because of contention on shared resources. Concurrently executing tasks mutually block their accesses to the shared resource, causing non-deterministic delays. Timing analysis of tasks in such systems is then far from trivial. Recently, several analytic methods have been proposed for this purpose, however, they cannot model complex arbitration schemes such as FlexRay which is a common bus arbitration protocol in the automotive industry. This paper considers real-time tasks composed of superblocks, i.e., sequences of computation and resource accessing phases. Resource accesses such as accesses to memories and caches are synchronous, i.e., they cause execution on the processing core to stall until the access is served. For such systems, the paper presents a state-based modeling and analysis approach based on Timed Automata which can model accurately arbitration schemes of any complexity. Based on it, we compute safe bounds on the worst-case response times of tasks. The scalability of the approach is increased significantly by abstracting several cores and their tasks with one arrival curve, which represents their resource accesses and computation times. This curve is then incorporated into the Timed Automata model of the system. The accuracy and scalability of the approach are evaluated with a real-world application from the automotive industry and benchmark applications.

SP2A: a service-oriented framework for P2P-based Grids

Abstract: Service-Oriented Architectures (SOAs) are rapidly becoming the key approach for achieving new levels of interoperability and scalability in the development of Grid applications. Within SOA solutions, current approaches for advertising service providers and for allowing prospective clients to discover them are mostly based on centralized registries. Envisioning Virtual Organizations in which all participants are both resource providers and consumers, in a peer-to-peer fashion, seems to be an appealing approach.In this paper we propose the Service-oriented Peer-to-Peer Architecture (SP2A), a framework enabling peer-to-peer resource sharing in Grid environments. Resources are not directly exposed but can be accessed through Resource Provision Services, whose semantically enriched interfaces are published in the network. The framework has been implemented as a Java API, which currently supports a number of important technologies such as JXTA (peer-to-peer routing), Web Services (service deployment), and OWL-S (semantic description of services).

Method for operating a computer cluster

Abstract: The invention allows for dealing with failures that may result in split-brain situations. In particular the safe management of shared resources is supported even though the owners of a shared resource may be subject to split-brain situation. In addition our invention allows us to update the cluster configuration despite the fact that some members of the cluster cannot be reached during the reconfiguration. The policies imposed by our invention ensure that all nodes started always use the up-to-date configuration as working configuration or if that is not possible the administrator is warned about a potential inconsistency of the configuration.